
# coding: utf-8

# In[15]:


import tensorflow as tf
import numpy as np


# In[26]:


xy = np.loadtxt('C:\\Users\\SANHA\\Desktop\\data\\data-04-zoo.csv', delimiter=',', dtype=np.float32)
x_data=xy[:,0:-1]
y_data=xy[:,[-1]]

nb_classes=7

X=tf.placeholder(tf.float32,[None,16])
Y=tf.placeholder(tf.int32,[None,1])

Y_one_hot=tf.one_hot(Y,nb_classes)
Y_one_hot=tf.reshape(Y_one_hot,[-1,nb_classes])

W=tf.Variable(tf.random_normal([16,nb_classes]),name='weight')
b=tf.Variable(tf.random_normal([nb_classes]),name='bias')

logits=tf.matmul(X,W)+b
hypothesis=tf.nn.softmax(logits)

cost_i=tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y_one_hot)

cost=tf.reduce_mean(cost_i)
optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

prediction=tf.argmax(hypothesis,1) #가능성을 퍼센트로~~
correct_prediction=tf.equal(prediction,tf.arg_max(Y_one_hot,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    for step in range(2000):
        sess.run(optimizer,feed_dict={X:x_data,Y:y_data})
        if step %100==0:
            loss,acc=sess.run([cost,accuracy],feed_dict={X:x_data,Y:y_data})
            print("step: {:5}\tLoss: {:.3f}\tAcc: {:.2%}".format(step,loss,acc))

    pred = sess.run(prediction, feed_dict={X: x_data})
    for p, y in zip(pred, y_data.flatten()):
            print("[{}] Prediction: {} True Y: {}".format(p == int(y), p, int(y)))

